将大语言模型整合进放射实践
这篇社论探讨大语言模型在放射学中的应用潜力与整合路径。文章由 ChatGPT-4 协助撰写并经人工审校，指出大语言模型可改善工作流效率、报告、教育与临床决策支持。整合需兼顾准确性、可靠性、伦理与临床监督，以平衡方式推进。
要点
- 大语言模型可辅助改善放射工作流效率与报告书写。
- 临床应用需关注准确性、可靠性及伦理问题。
- 整合应保留适当临床监督与持续评估。
核心结论
- 大语言模型整合进放射学需要在拥抱创新能力的同时确保伦理、安全与实用实施。
- 在适当保障与持续评估下，大语言模型可能有助于改善患者照护与结局。
研究设计与样本
- 本文为社论，由 ChatGPT-4 辅助撰写并经作者审校、编辑与参考文献核实。
临床应用与阅片要点
- 可将大语言模型用于报告书写辅助，但需人工审核内容。
- 临床决策支持使用时必须保留放射科医生判断与监督。
局限性
- 大语言模型应用存在准确性、可靠性及伦理使用方面的顾虑。
- 整合需关注适当临床监督，避免不加审查直接采用模型输出。
以上解读依据论文摘要（全文未开放获取或许可不允许翻译转载）。
英文原文（摘要）· 点击展开
PURPOSE: To highlight the potential of large language models (LLMs) in radiology and to stimulate discussion on their integration into clinical practice, including associated benefits and challenges.
METHODS: This editorial was authored with the assistance of an advanced artificial intelligence (AI) language model (ChatGPT-4, OpenAI), under direct human oversight. The content was critically reviewed, edited, and refined by the author, and all references were verified by the editors.
RESULTS: LLMs demonstrate substantial potential to enhance radiological practice, including improvements in workflow efficiency, reporting, education, and clinical decision support. However, their implementation raises important considerations related to accuracy, reliability, ethical use, and appropriate clinical oversight.
CONCLUSION: The integration of LLMs into radiology requires a balanced approach that embraces their innovative capabilities while ensuring ethical, safe, and practical implementation. With appropriate safeguards and continued evaluation, LLMs may contribute to improved patient care and outcomes.
引用
- van Assen, Muscogiuri, De Cecco, et al. Integrating large language models into radiological practice. Eur J Radiol. 2026. PMID: 42302435. doi:10.1016/j.ejrad.2026.112983
供执业医师工作参考，不替代临床判断；数值与分级以指南原文为准。